



'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     :						Dict	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						int	=  {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
SCREAMING_SNAKE_CASE__     :						Dict	=  [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 				a  (     UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    Dict      ,					UpperCamelCase_    :    List[Any]      ,					UpperCamelCase_    :    Dict      ,					UpperCamelCase_    :    str			)       ->	str:
		for attribute in key.split('.'			):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								snake_case__	     ='lm_head'

				snake_case__	     =getattr(UpperCamelCase_      ,					UpperCamelCase_			)

		if weight_type is not None:
				snake_case__	     =getattr(UpperCamelCase_      ,					UpperCamelCase_			).shape
		else:
				snake_case__	     =hf_pointer.shape

		assert hf_shape == value.shape, (
		    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
		    f""" {value.shape} for {full_name}"""
		)

		if weight_type == "weight":
				snake_case__	     =value
		elif weight_type == "weight_g":
				snake_case__	     =value
		elif weight_type == "weight_v":
				snake_case__	     =value
		elif weight_type == "bias":
				snake_case__	     =value
		else:
				snake_case__	     =value

		logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""			)






def 				a  (     UpperCamelCase_    :    Optional[int]      ,					UpperCamelCase_    :    List[Any]      ,					UpperCamelCase_    :    str			)       ->	Optional[int]:
		snake_case__	     =[]
		snake_case__	     =fairseq_model.state_dict()

		snake_case__	     =hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				snake_case__	     =False
				if "conv_layers" in name:
						load_conv_layer(
						    UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					hf_model.config.feat_extract_norm == 'group'      ,					)
						snake_case__	     =True
				else:
						for key, mapped_key in MAPPING.items():
								snake_case__	     ='unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
										snake_case__	     =True
										if "*" in mapped_key:
												snake_case__	     =name.split(UpperCamelCase_			)[0].split('.'			)[-2]
												snake_case__	     =mapped_key.replace('*'      ,					UpperCamelCase_			)
										if "weight_g" in name:
												snake_case__	     ='weight_g'
										elif "weight_v" in name:
												snake_case__	     ='weight_v'
										elif "bias" in name:
												snake_case__	     ='bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												snake_case__	     ='weight'
										else:
												snake_case__	     =None
										set_recursively(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)
								continue
				if not is_used:
						unused_weights.append(UpperCamelCase_			)

		logger.warning(f"""Unused weights: {unused_weights}"""			)






def 				a  (     UpperCamelCase_    :    Tuple      ,					UpperCamelCase_    :    str      ,					UpperCamelCase_    :    int      ,					UpperCamelCase_    :    Tuple      ,					UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =full_name.split('conv_layers.'			)[-1]
		snake_case__	     =name.split('.'			)
		snake_case__	     =int(items[0]			)
		snake_case__	     =int(items[1]			)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    f"""{full_name} has size {value.shape}, but"""
						    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
						)
						snake_case__	     =value
						logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    f"""{full_name} has size {value.shape}, but"""
						    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
						)
						snake_case__	     =value
						logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""			)
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
						    " found."
						)
						snake_case__	     =value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    f"""{full_name} has size {value.shape}, but"""
						    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
						)
						snake_case__	     =value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
		else:
				unused_weights.append(UpperCamelCase_			)






@torch.no_grad()
def 				a  (     UpperCamelCase_    :    List[Any]      ,					UpperCamelCase_    :    Tuple      ,					UpperCamelCase_    :    Dict=None      ,					UpperCamelCase_    :    Tuple=None      ,					UpperCamelCase_    :    Optional[int]=True			)       ->	Any:
		if config_path is not None:
				snake_case__	     =UniSpeechConfig.from_pretrained(UpperCamelCase_			)
		else:
				snake_case__	     =UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						snake_case__	     =Dictionary.load_from_json(UpperCamelCase_			)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						snake_case__	     =target_dict.pad_index
						snake_case__	     =target_dict.bos_index
						snake_case__	     =target_dict.eos_index
						snake_case__	     =len(target_dict.symbols			)
						snake_case__	     =os.path.join(UpperCamelCase_      ,					'vocab.json'			)
						if not os.path.isdir(UpperCamelCase_			):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCamelCase_			)			)
								return
						os.makedirs(UpperCamelCase_      ,					exist_ok=UpperCamelCase_			)
						snake_case__	     =target_dict.indices

						# fairseq has the <pad> and <s> switched
						snake_case__	     =42
						snake_case__	     =43
						with open(UpperCamelCase_      ,					'w'      ,					encoding='utf-8'			) as vocab_handle:
								json.dump(UpperCamelCase_      ,					UpperCamelCase_			)
						snake_case__	     =WavaVecaPhonemeCTCTokenizer(
						    UpperCamelCase_      ,					unk_token=target_dict.unk_word      ,					pad_token=target_dict.pad_word      ,					bos_token=target_dict.bos_word      ,					eos_token=target_dict.eos_word      ,					word_delimiter_token='|'      ,					do_lower_case=UpperCamelCase_      ,					)
						snake_case__	     =True if config.feat_extract_norm == 'layer' else False
						snake_case__	     =WavaVecaFeatureExtractor(
						    feature_size=1      ,					sampling_rate=16000      ,					padding_value=0      ,					do_normalize=UpperCamelCase_      ,					return_attention_mask=UpperCamelCase_      ,					)
						snake_case__	     =WavaVecaProcessor(feature_extractor=UpperCamelCase_      ,					tokenizer=UpperCamelCase_			)
						processor.save_pretrained(UpperCamelCase_			)

				snake_case__	     =UniSpeechForCTC(UpperCamelCase_			)
		else:
				snake_case__	     =UniSpeechForPreTraining(UpperCamelCase_			)

		if is_finetuned:
				snake_case__     ,	snake_case__     ,	snake_case__	     =fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]      ,					arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			), 'w2v_path': checkpoint_path}			)
		else:
				snake_case__     ,	snake_case__     ,	snake_case__	     =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

		snake_case__	     =model[0].eval()

		recursively_load_weights(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)

		hf_unispeech.save_pretrained(UpperCamelCase_			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						str	=  argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument(
			    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
			)
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  parser.parse_args()
			convert_unispeech_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)









'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    int			)       ->	tuple[float, list[float]]:
		snake_case__	     =list(range(len(UpperCamelCase_			)			)			)
		snake_case__	     =[v / w for v, w in zip(UpperCamelCase_      ,					UpperCamelCase_			)]
		index.sort(key=lambda UpperCamelCase_			: ratio[i]      ,					reverse=UpperCamelCase_			)

		snake_case__	     =0
		snake_case__	     =[0] * len(UpperCamelCase_			)
		for i in index:
				if weight[i] <= capacity:
						snake_case__	     =1
						max_value += value[i]
						capacity -= weight[i]
				else:
						snake_case__	     =capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()




